Concept-based document readability in domain specific information retrieval

Domain specific information retrieval has become in demand. Not only domain experts, but also average non-expert users are interested in searching domain specific (e.g., medical and health) information from online resources. However, a typical problem to average users is that the search results are always a mixture of documents with different levels of readability. Non-expert users may want to see documents with higher readability on the top of the list. Consequently the search results need to be re-ranked in a descending order of readability. It is often not practical for domain experts to manually label the readability of documents for large databases. Computational models of readability needs to be investigated. However, traditional readability formulas are designed for general purpose text and insufficient to deal with technical materials for domain specific information retrieval. More advanced algorithms such as textual coherence model are computationally expensive for re-ranking a large number of retrieved documents. In this paper, we propose an effective and computationally tractable concept-based model of text readability. In addition to textual genres of a document, our model also takes into account domain specific knowledge, i.e., how the domain-specific concepts contained in the document affect the document’s readability. Three major readability formulas are proposed and applied to health and medical information retrieval. Experimental results show that our proposed readability formulas lead to remarkable improvements in terms of correlation with users’ readability ratings over four traditional readability measures.

Refinement-robust fairness

We motivate and study the robustness of fairness notions under refinement of transitions and places in Petri nets. We show that the classical notions of weak and strong fairness are not robust and we propose a hierarchy of increasingly strong, refinement-robust fairness notions. That hierarchy is based on the conflict structure of transitions, which characterizes the interplay between choice and synchronization in a fairness notion. Our fairness notions are defined on non-sequential runs, but we show that the most important notions can be easily expressed on sequential runs as well. The hierarchy is further motivated by a brief discussion on the computational power of the fairness notions.